Journal
IEEE TRANSACTIONS ON POWER SYSTEMS
Volume 35, Issue 6, Pages 4655-4665Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRS.2020.3000577
Keywords
Power system stability; Eigenvalues and eigenfunctions; Probabilistic logic; Random variables; Wind speed; Generators; Wind generation; solar PV; probabilistic analysis; Monte Carlo simulation; modal analysis; controllability; small-signal stability; Weibull distribution; Kernel density estimation (KDE)
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Funding
- National Research Foundation of Korea [2017K1A4A3013579]
- National Research Foundation of Korea (NRF)
- Korea government (MSIT) [2018R1C1B5084745]
- National Research Foundation of Korea [2018R1C1B5084745, 2017K1A4A3013579] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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This article proposes a framework for stochastic eigenvalue analysis of electric power systems with a high penetration of inertialess renewable generation, focusing on the influential factors that affect the eigenvalue movement resulting from the inertia reduction. We analytically investigate the influence of the inertia and the variation in renewable generation on small-signal stability using stochastic Monte-Carlo based eigenvalue and modal controllability analysis. With the increasing penetration of renewable generation, power system behavior depends on meteorological conditions more, which results in the reduction of power system inertia due to the decommitment of generators and a consequent deterioration of power system stability. Against this backdrop, stochastic eigenvalue analysis is carried out to examine the movement of eigenvalues resulting from the variable operating conditions. The contribution of the generators to the oscillatory modes is theoretically proved using modal controllability in a power system with reduced inertia. For the verification of the research, the simulation is carried out using DIgSILENT/PowerFactory.
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